---
title: "ggplotly geoms"
author: "Carson Sievert"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    social: menu
    source_code: embed
---

```{r setup, include=FALSE}
library(ggplot2)
library(plotly)
library(plyr)
library(flexdashboard)

# Make some noisily increasing data
set.seed(955)
dat <- data.frame(cond = rep(c("A", "B"), each=10),
                  xvar = 1:20 + rnorm(20,sd=3),
                  yvar = 1:20 + rnorm(20,sd=3))
```

geom_point
=======================================================================

Row
-----------------------------------------------------------------------

### Scatter Chart with geom_point

```{r}
p <- ggplot(dat, aes(x=xvar, y=yvar)) +
            geom_point(shape=1)      # Use hollow circles
ggplotly(p)
```


### geom_smooth Linear Regression

```{r}
p <- ggplot(dat, aes(x=xvar, y=yvar)) +
            geom_point(shape=1) +    # Use hollow circles
            geom_smooth(method=lm)   # Add linear regression line
ggplotly(p)
```

Row
-----------------------------------------------------------------------

### geom_smooth with Loess Smoothed Fit

```{r}
p <- ggplot(dat, aes(x=xvar, y=yvar)) +
            geom_point(shape=1) +    # Use hollow circles
            geom_smooth()            # Add a loess smoothed fit curve with confidence region
ggplotly(p)
```

### Constraining Slope with stat_smooth

```{r, eval = F}
n <- 20
x1 <- rnorm(n); x2 <- rnorm(n)
y1 <- 2 * x1 + rnorm(n)
y2 <- 3 * x2 + (2 + rnorm(n))
A <- as.factor(rep(c(1, 2), each = n))
df <- data.frame(x = c(x1, x2), y = c(y1, y2), A = A)
fm <- lm(y ~ x + A, data = df)

p <- ggplot(data = cbind(df, pred = predict(fm)), aes(x = x, y = y, color = A))
p <- p + geom_point() + geom_line(aes(y = pred))
ggplotly(p)
```

This cell will now be filled with lots of text.

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Tab_2
=======================================================================

Row
-----------------------------------------------------------------------

### Scatter Chart with geom_point

```{r}
p <- ggplot(dat, aes(x=xvar, y=yvar)) +
            geom_point(shape=1)      # Use hollow circles
ggplotly(p)
```


### geom_smooth Linear Regression

```{r}
p <- ggplot(dat, aes(x=xvar, y=yvar)) +
            geom_point(shape=1) +    # Use hollow circles
            geom_smooth(method=lm)   # Add linear regression line
ggplotly(p)
```

Row
-----------------------------------------------------------------------

### geom_smooth with Loess Smoothed Fit

```{r}
p <- ggplot(dat, aes(x=xvar, y=yvar)) +
            geom_point(shape=1) +    # Use hollow circles
            geom_smooth()            # Add a loess smoothed fit curve with confidence region
ggplotly(p)
```

### Constraining Slope with stat_smooth

```{r, eval = F}
n <- 20
x1 <- rnorm(n); x2 <- rnorm(n)
y1 <- 2 * x1 + rnorm(n)
y2 <- 3 * x2 + (2 + rnorm(n))
A <- as.factor(rep(c(1, 2), each = n))
df <- data.frame(x = c(x1, x2), y = c(y1, y2), A = A)
fm <- lm(y ~ x + A, data = df)

p <- ggplot(data = cbind(df, pred = predict(fm)), aes(x = x, y = y, color = A))
p <- p + geom_point() + geom_line(aes(y = pred))
ggplotly(p)
```

This cell will now be filled with lots of text.

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Integer nec odio. Praesent libero. Sed cursus ante dapibus diam. Sed nisi. Nulla quis sem at nibh elementum imperdiet. Duis sagittis ipsum. Praesent mauris. Fusce nec tellus sed augue semper porta. Mauris massa. Vestibulum lacinia arcu eget nulla. 

Class aptent taciti sociosqu ad litora torquent per conubia nostra, per inceptos himenaeos. Curabitur sodales ligula in libero. Sed dignissim lacinia nunc. Curabitur tortor. Pellentesque nibh. Aenean quam. In scelerisque sem at dolor. Maecenas mattis. Sed convallis tristique sem. Proin ut ligula vel nunc egestas porttitor. Morbi lectus risus, iaculis vel, suscipit quis, luctus non, massa. Fusce ac turpis quis ligula lacinia aliquet. Mauris ipsum.